Selected Variables

base: Code of the patient
covariates:
- Age
- Gender
- Prior Spine Surgery
- '1st surgeon: experience in ASD surgery'
- ASA classification
- Decompression
- Osteotomy
- 3CO
- SPOs
- BMI_First Visit
- Tobacco use_First Visit
- Osteoporosis / osteopenia
- Levels Previously operated - Lower
- LGap
- RLL
- RSA
- RPV
- Cobb LS curve (Degree)
- Number of Interbody Fusions
- 'Posterior Instrumented Fusion: Upper / Lower Levels'
- Alif
- LL-Lordosis Difference
outcomes_ql:
- 2Y. ODI - Score (%)
- 2Y. SRS22 - SRS Subtotal score
- 2Y. SF36 - MCS
- 2Y. SF36 - PCS
outcomes_radiology:
- 6W. Major curve Cobb angle
- 1Y. Major curve Cobb angle
- 6W. T1 Sagittal Tilt
- 1Y. T1 Sagittal Tilt
- 6W. Sagittal Balance
- 1Y. Sagittal Balance
- 6W. Global Tilt
- 1Y. Global Tilt
- 6W. Lordosis (top of L1-S1)
- 1Y. Lordosis (top of L1-S1)
- 6W. LGap
- 1Y. LGap
- 6W. Pelvic Tilt
- 1Y. Pelvic Tilt
- 6W. RSA
- 1Y. RSA
- 6W. RPV
- 1Y. RPV
- 6W. RLL
- 1Y. RLL
predictive:
- Weight (kgs)_First Visit
- Height (cm)_First Visit
- Total surgical time st1+st2+st3
- Osteotomy
- Alcohol/drug abuse
- Anemia or other blood disorders
- Osteoarthritis
- Mild vascular
- Depression / anxiety
- Diabetes with end organ damage
- Cardiac
- Hypertension
- Chronic pulmonary disease
- Nervous system disorders
- Renal
- Peripheral vascular disease
- Psychiatric / Behavioral
- Peptic ulcer
- Bladder incontinence
- Bowel incontinence
- Leg weakness
- Loss of balance
- NRS back - Leg pain - Average
- Tobacco use_First Visit
- Years with spine problems
- ODI - Score (%)_First Visit
- SRS22 - SRS Total score_First Visit
- SF36 - PCS_First Visit
- SF36 - MCS_First Visit
- Major curve Cobb angle
demographic:
- Age
- Gender
- Prior Spine Surgery
- ASA classification
- 3CO
- BMI_First Visit
- Global Tilt
- Ideal LL
- Lordosis (top of L1-S1)
- ODI - Score (%)_First Visit
- SRS22 - SRS Total score_First Visit
- SF36 - PCS_First Visit
- SF36 - MCS_First Visit
- Major curve Cobb angle
expanded:
- Age
- Gender
- Prior Spine Surgery
- '1st surgeon: experience in ASD surgery'
- ASA classification
- Decompression
- Osteotomy
- 3CO
- SPOs
- BMI_First Visit
- Tobacco use_First Visit
- Osteoporosis / osteopenia
- Levels Previously operated - Lower
- LGap
- RLL
- RSA
- RPV
- Cobb LS curve (Degree)
- Number of Interbody Fusions
- 'Posterior Instrumented Fusion: Upper / Lower Levels'
- Alif
- LL-Lordosis Difference
- Weight (kgs)_First Visit
- Height (cm)_First Visit
- Total surgical time st1+st2+st3
- Alcohol/drug abuse
- Anemia or other blood disorders
- Osteoarthritis
- Mild vascular
- Depression / anxiety
- Diabetes with end organ damage
- Cardiac
- Hypertension
- Chronic pulmonary disease
- Nervous system disorders
- Renal
- Peripheral vascular disease
- Psychiatric / Behavioral
- Peptic ulcer
- Bladder incontinence
- Bowel incontinence
- Leg weakness
- Loss of balance
- NRS back - Leg pain - Average
- Years with spine problems
- ODI - Score (%)_First Visit
- SRS22 - SRS Total score_First Visit
- SF36 - PCS_First Visit
- SF36 - MCS_First Visit
- Major curve Cobb angle
- SRS22 - SRS Subtotal score_First Visit
- T1 Sagittal Tilt
- Sagittal Balance
- Global Tilt
- Lordosis (top of L1-S1)
- Pelvic Tilt

Propensity Scores Common Support

Model Stats

  • Treatment proportion: 0.118
  • Model Type: elastic_net
  • Accuracy: 0.9031159
  • Params: alpha: 0.4230769 lambda: 0.0101856

Average Treatment Effects - Radiology

Outcome: 6W. Major curve Cobb angle
Distribution:
      0%      25%      50%      75%     100% 
-72.0000 -21.0000 -10.9750  -4.0575  27.5500 
Model Type Y: boosting 
RMSE: 19.1344565668347 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.75

Model Type No: boosting 
RMSE: 13.4254248524173 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.625

ATE (Yes-No): -3.576 (Std.Error: 7.107)
Trimmed ATE (Yes-No): -3.506 (Std.Error: 7.369)
Upper ATE (Yes-No): -5.522 (Std.Error: 6.176)
Observational differences in treatment 2.391 (Yes-No) 

   treatment  outcome
1:       Yes 23.47162
2:        No 21.08053
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
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`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
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Outcome: 1Y. Major curve Cobb angle
Distribution:
      0%      25%      50%      75%     100% 
-64.0000 -22.7375 -10.0500  -3.0000  22.4400 
Model Type Y: boosting 
RMSE: 22.5669927753426 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 14.1922311651887 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.875

ATE (Yes-No): -0.426 (Std.Error: 10.941)
Trimmed ATE (Yes-No): -0.021 (Std.Error: 11.423)
Upper ATE (Yes-No): -11.369 (Std.Error: 6.144)
Observational differences in treatment 3.58 (Yes-No) 

   treatment  outcome
1:       Yes 24.42677
2:        No 20.84643
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
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`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
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Outcome: 6W. T1 Sagittal Tilt
Distribution:
        0%        25%        50%        75%       100% 
-23.631420  -6.000000  -1.527972   1.502515  18.000000 
Model Type Y: boosting 
RMSE: 8.00289948388854 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 6.12123833599355 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.75

ATE (Yes-No): -4.33 (Std.Error: 1.615)
Trimmed ATE (Yes-No): -4.498 (Std.Error: 1.691)
Upper ATE (Yes-No): 0.672 (Std.Error: 4.396)
Observational differences in treatment -1.739 (Yes-No) 

   treatment   outcome
1:       Yes -4.404929
2:        No -2.665563
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
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`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
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Outcome: 1Y. T1 Sagittal Tilt
Distribution:
        0%        25%        50%        75%       100% 
-30.098675  -6.000000  -2.041798   1.028620  20.000000 
Model Type Y: boosting 
RMSE: 8.80724662082878 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 5.82580116506636 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -2.531 (Std.Error: 2.274)
Trimmed ATE (Yes-No): -2.528 (Std.Error: 2.366)
Upper ATE (Yes-No): -2.607 (Std.Error: 4.089)
Observational differences in treatment -1.506 (Yes-No) 

   treatment   outcome
1:       Yes -4.114989
2:        No -2.609148
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
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Outcome: 6W. Sagittal Balance
Distribution:
       0%       25%       50%       75%      100% 
-194.7900  -69.0225  -30.4450   -1.2200   89.0000 
Model Type Y: boosting 
RMSE: 63.3740808935941 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 53.6521665047472 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.75

ATE (Yes-No): -29.641 (Std.Error: 12.47)
Trimmed ATE (Yes-No): -30.985 (Std.Error: 12.798)
Upper ATE (Yes-No): 2.472 (Std.Error: 29.087)
Observational differences in treatment -15.226 (Yes-No) 

   treatment  outcome
1:       Yes 18.15556
2:        No 33.38130
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
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Outcome: 1Y. Sagittal Balance
Distribution:
     0%     25%     50%     75%    100% 
-237.47  -67.36  -30.50    6.00   89.37 
Model Type Y: boosting 
RMSE: 65.6872254086815 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.625

Model Type No: boosting 
RMSE: 51.6153772155296 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.75

ATE (Yes-No): -12.57 (Std.Error: 16.526)
Trimmed ATE (Yes-No): -11.579 (Std.Error: 16.916)
Upper ATE (Yes-No): -38.138 (Std.Error: 41.537)
Observational differences in treatment -18.519 (Yes-No) 

   treatment  outcome
1:       Yes 19.17207
2:        No 37.69094
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
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Outcome: 6W. Global Tilt
Distribution:
      0%      25%      50%      75%     100% 
-68.6200 -18.1775  -6.0900   1.8600 149.4100 
Model Type Y: boosting 
RMSE: 14.6199142103517 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 14.589574194843 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.625

ATE (Yes-No): -4.807 (Std.Error: 2.954)
Trimmed ATE (Yes-No): -4.722 (Std.Error: 3.001)
Upper ATE (Yes-No): -7.085 (Std.Error: 6.733)
Observational differences in treatment -7.066 (Yes-No) 

   treatment  outcome
1:       Yes 18.44622
2:        No 25.51221
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
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Outcome: 1Y. Global Tilt
Distribution:
      0%      25%      50%      75%     100% 
-62.6300 -16.5425  -5.8950   1.0000  26.0000 
Model Type Y: boosting 
RMSE: 17.2928369500607 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 11.4472625886298 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.625

ATE (Yes-No): -7.248 (Std.Error: 5.678)
Trimmed ATE (Yes-No): -7.048 (Std.Error: 5.877)
Upper ATE (Yes-No): -12.174 (Std.Error: 9.123)
Observational differences in treatment -5.202 (Yes-No) 

   treatment  outcome
1:       Yes 20.72767
2:        No 25.92920
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
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Outcome: 6W. Lordosis (top of L1-S1)
Distribution:
     0%     25%     50%     75%    100% 
-94.930 -24.250  -9.975   0.000  29.000 
Model Type Y: boosting 
RMSE: 20.11819476937 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.625

Model Type No: boosting 
RMSE: 16.5649106978736 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.75

ATE (Yes-No): -0.607 (Std.Error: 5.188)
Trimmed ATE (Yes-No): -0.042 (Std.Error: 5.35)
Upper ATE (Yes-No): -16.478 (Std.Error: 10.581)
Observational differences in treatment -1.922 (Yes-No) 

   treatment   outcome
1:       Yes -51.48541
2:        No -49.56293
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
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`geom_smooth()` using formula 'y ~ x'
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Outcome: 1Y. Lordosis (top of L1-S1)
Distribution:
    0%    25%    50%    75%   100% 
-94.63 -25.00  -8.02   0.00  23.38 
Model Type Y: boosting 
RMSE: 26.1636384789115 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.875

Model Type No: boosting 
RMSE: 15.0756215614596 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.625

ATE (Yes-No): -6.624 (Std.Error: 8.902)
Trimmed ATE (Yes-No): -6.111 (Std.Error: 9.12)
Upper ATE (Yes-No): -20.521 (Std.Error: 16.108)
Observational differences in treatment 1.517 (Yes-No) 

   treatment   outcome
1:       Yes -47.94133
2:        No -49.45805
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
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Outcome: 6W. LGap
Distribution:
      0%      25%      50%      75%     100% 
-96.1234 -24.7700  -9.4550   0.3811  78.9200 
Model Type Y: boosting 
RMSE: 20.42493941694 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 17.3771303705552 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5

ATE (Yes-No): -2.55 (Std.Error: 6.595)
Trimmed ATE (Yes-No): -2.312 (Std.Error: 6.71)
Upper ATE (Yes-No): -9.206 (Std.Error: 9.606)
Observational differences in treatment -3.676 (Yes-No) 

   treatment  outcome
1:       Yes 10.34216
2:        No 14.01857
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
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`geom_smooth()` using formula 'y ~ x'
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Outcome: 1Y. LGap
Distribution:
      0%      25%      50%      75%     100% 
-94.8082 -25.0000  -8.0968   0.1456  22.0800 
Model Type Y: boosting 
RMSE: 24.905181099653 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 15.3550171098447 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

ATE (Yes-No): -6.687 (Std.Error: 7.367)
Trimmed ATE (Yes-No): -6.181 (Std.Error: 7.639)
Upper ATE (Yes-No): -20.2 (Std.Error: 15.164)
Observational differences in treatment -1.011 (Yes-No) 

   treatment  outcome
1:       Yes 12.61173
2:        No 13.62279
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
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`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
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Outcome: 6W. Pelvic Tilt
Distribution:
      0%      25%      50%      75%     100% 
-36.4100  -8.3125  -2.2850   2.1250  14.4200 
Model Type Y: boosting 
RMSE: 9.5492491892606 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 7.49351153163695 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.875

ATE (Yes-No): -1.442 (Std.Error: 2.97)
Trimmed ATE (Yes-No): -1.173 (Std.Error: 3.045)
Upper ATE (Yes-No): -9.734 (Std.Error: 3.766)
Observational differences in treatment -3.76 (Yes-No) 

   treatment  outcome
1:       Yes 18.29917
2:        No 22.05950
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. Pelvic Tilt
Distribution:
    0%    25%    50%    75%   100% 
-26.62  -7.00  -2.01   2.00  23.00 
Model Type Y: boosting 
RMSE: 11.4387146979675 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 6.64098273733944 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -6.155 (Std.Error: 2.831)
Trimmed ATE (Yes-No): -6.247 (Std.Error: 2.946)
Upper ATE (Yes-No): -3.712 (Std.Error: 5.116)
Observational differences in treatment -3.067 (Yes-No) 

   treatment  outcome
1:       Yes 19.68833
2:        No 22.75583
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 6W. RSA
Distribution:
      0%      25%      50%      75%     100% 
-67.5592 -18.1199  -6.2346   2.0409  76.5028 
Model Type Y: boosting 
RMSE: 15.4981167923119 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 12.9443970463766 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5

ATE (Yes-No): -4.684 (Std.Error: 3.533)
Trimmed ATE (Yes-No): -4.553 (Std.Error: 3.599)
Upper ATE (Yes-No): -8.169 (Std.Error: 6.838)
Observational differences in treatment -5.549 (Yes-No) 

   treatment   outcome
1:       Yes  7.430605
2:        No 12.979607
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. RSA
Distribution:
      0%      25%      50%      75%     100% 
-62.4716 -16.5397  -5.9012   1.0000  25.0400 
Model Type Y: boosting 
RMSE: 17.2089375156061 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.625

Model Type No: boosting 
RMSE: 11.0555975084751 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.75

ATE (Yes-No): -6.871 (Std.Error: 5.075)
Trimmed ATE (Yes-No): -6.663 (Std.Error: 5.216)
Upper ATE (Yes-No): -11.984 (Std.Error: 6.566)
Observational differences in treatment -2.682 (Yes-No) 

   treatment  outcome
1:       Yes 10.84495
2:        No 13.52673
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 6W. RPV
Distribution:
        0%        25%        50%        75%       100% 
-85.555100  -2.274800   2.157300   8.233225  35.503900 
Model Type Y: boosting 
RMSE: 9.37456977983556 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 8.64274202597782 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.75

ATE (Yes-No): 1.113 (Std.Error: 1.884)
Trimmed ATE (Yes-No): 0.947 (Std.Error: 1.971)
Upper ATE (Yes-No): 5.751 (Std.Error: 5.121)
Observational differences in treatment 3.999 (Yes-No) 

   treatment   outcome
1:       Yes -4.583197
2:        No -8.582035
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. RPV
Distribution:
        0%        25%        50%        75%       100% 
-22.180000  -1.385350   2.384400   6.666725  26.634600 
Model Type Y: boosting 
RMSE: 11.1541854335375 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.625

Model Type No: boosting 
RMSE: 6.446843469848 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): 5.253 (Std.Error: 2.848)
Trimmed ATE (Yes-No): 5.302 (Std.Error: 2.93)
Upper ATE (Yes-No): 3.925 (Std.Error: 4.46)
Observational differences in treatment 1.018 (Yes-No) 

   treatment   outcome
1:       Yes -7.435010
2:        No -8.453427
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 6W. RLL
Distribution:
      0%      25%      50%      75%     100% 
-87.1818  -0.2649   9.4794  24.9050  96.3002 
Model Type Y: boosting 
RMSE: 22.1153714297089 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 17.0135844061194 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.875

ATE (Yes-No): 1.244 (Std.Error: 4.591)
Trimmed ATE (Yes-No): 0.783 (Std.Error: 4.772)
Upper ATE (Yes-No): 14.104 (Std.Error: 8.886)
Observational differences in treatment 3.723 (Yes-No) 

   treatment   outcome
1:       Yes -11.11809
2:        No -14.84127
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. RLL
Distribution:
      0%      25%      50%      75%     100% 
-22.5800  -0.3774   8.0504  25.0426  94.8346 
Model Type Y: boosting 
RMSE: 25.6400920329949 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.75

Model Type No: boosting 
RMSE: 16.1664919958679 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.625

ATE (Yes-No): 5.757 (Std.Error: 7.573)
Trimmed ATE (Yes-No): 5.124 (Std.Error: 7.807)
Upper ATE (Yes-No): 22.654 (Std.Error: 12.366)
Observational differences in treatment 1.402 (Yes-No) 

   treatment   outcome
1:       Yes -13.19885
2:        No -14.60109
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'